Core Viewpoint - The article discusses the differences in semantic compression strategies between large language models (LLMs) and human cognition, highlighting that LLMs focus on statistical compression while humans prioritize detail and context [4][17]. Group 1: Semantic Compression - Semantic compression allows efficient organization of knowledge and quick categorization of the world [3]. - A new information-theoretic framework was proposed to compare the strategies of humans and LLMs in semantic compression [4]. - The study reveals fundamental differences in compression efficiency and semantic fidelity between LLMs and humans, with LLMs leaning towards extreme statistical compression [5][17]. Group 2: Research Methodology - The research team established a robust human concept classification benchmark based on classic cognitive science studies, covering 1,049 items across 34 semantic categories [5][6]. - The dataset provides category affiliation information and human ratings of "typicality," reflecting deep structures in human cognition [6][7]. - Over 30 LLMs were selected for evaluation, with parameter sizes ranging from 300 million to 72 billion, ensuring a fair comparison with human cognitive benchmarks [8]. Group 3: Findings and Implications - The study found that LLMs' concept classification results align significantly better with human semantic classification than random levels, validating LLMs' basic capabilities in semantic organization [10][11]. - However, LLMs struggle with fine-grained semantic differences, indicating a mismatch between their internal concept structures and human intuitive category assignments [14][16]. - The research highlights that LLMs prioritize reducing redundant information, while humans emphasize adaptability and richness, maintaining context integrity [17]. Group 4: Research Contributors - The research was conducted collaboratively by Stanford University and New York University, with Chen Shani as the lead author [19][20]. - Yann LeCun, a prominent figure in AI and a co-author of the study, has significantly influenced the evolution of AI technologies [24][25][29].